Constrained Disorder Principle-Based Second-Generation Algorithms Implement Quantified Variability Signatures to Improve the Function of Complex Systems

Article Information

Tal Sigawi, Hillel Lehman, Noa Hurvitz, Yaron Ilan1*

1Faculty of Medicine, Hebrew University and Department of Medicine, Hadassah Medical Center, Jerusalem, Israel

*Corresponding author: Yaron Ilan, MD Faculty of Medicine, Hebrew University Director, Department of Medicine, Hadassah Medical Center Jerusalem, Israel, POB 12000, IL91120.

*The first three authors contributed equally

Received: 10 March 2023; Accepted: 17 March 2023; Published: 03 April 2023


Tal Sigawi, Hillel Lehmann, Noa Hurvitz, Yaron Ilan. Constrained disorder principle-based second-generation algorithms implement quantified variability signatures to improve the function of complex systems. Journal of Bioinformatics and Systems Biology. 6 (2023): 82-89.

View / Download Pdf Share at Facebook


Improving the efficacy and overcoming the malfunctions of systems are significant challenges. Variability characterizes all levels of complex biological systems. We reviewed the relevant publications and described a method for improving the systems' function. The constrained disorder principle (CDP) defines the function of living systems based on their degree of variability. Per the CDP, the boundaries of a system define its function and efficiency. The present paper aims to describe the role of variability in biological systems and the generation of CDP-based second-generation artificial intelligence (AI) algorithms designed to improve effectiveness and correct malfunctions of biological organisms by focusing on implementing personalized variability signatures. The paper describes some of the challenges of first-generation AI systems, focusing on the three steps process of establishing the second-generation platforms comprising: the use of a pseudorandom number generator in an openloop system, implementing variability based on feedback in a closed-loop system, and quantifying variability signatures in a personalized way for improving algorithm' output. Examples of its use in humans are provided. The CDP provides a platform for improving disturbed systems' functions using second-generation AI systems.


artificial intelligence, variability, defective engineering, complex systems;

artificial intelligence articles; variability articles; defective engineering articles; complex systems articles;

artificial intelligence articles artificial intelligence Research articles artificial intelligence review articles artificial intelligence PubMed articles artificial intelligence PubMed Central articles artificial intelligence 2023 articles artificial intelligence 2024 articles artificial intelligence Scopus articles artificial intelligence impact factor journals artificial intelligence Scopus journals artificial intelligence PubMed journals artificial intelligence medical journals artificial intelligence free journals artificial intelligence best journals artificial intelligence top journals artificial intelligence free medical journals artificial intelligence famous journals artificial intelligence Google Scholar indexed journals variability articles variability Research articles variability review articles variability PubMed articles variability PubMed Central articles variability 2023 articles variability 2024 articles variability Scopus articles variability impact factor journals variability Scopus journals variability PubMed journals variability medical journals variability free journals variability best journals variability top journals variability free medical journals variability famous journals variability Google Scholar indexed journals defective engineering articles defective engineering Research articles defective engineering review articles defective engineering PubMed articles defective engineering PubMed Central articles defective engineering 2023 articles defective engineering 2024 articles defective engineering Scopus articles defective engineering impact factor journals defective engineering Scopus journals defective engineering PubMed journals defective engineering medical journals defective engineering free journals defective engineering best journals defective engineering top journals defective engineering free medical journals defective engineering famous journals defective engineering Google Scholar indexed journals complex systems articles complex systems Research articles complex systems review articles complex systems PubMed articles complex systems PubMed Central articles complex systems 2023 articles complex systems 2024 articles complex systems Scopus articles complex systems impact factor journals complex systems Scopus journals complex systems PubMed journals complex systems medical journals complex systems free journals complex systems best journals complex systems top journals complex systems free medical journals complex systems famous journals complex systems Google Scholar indexed journals CDP articles CDP Research articles CDP review articles CDP PubMed articles CDP PubMed Central articles CDP 2023 articles CDP 2024 articles CDP Scopus articles CDP impact factor journals CDP Scopus journals CDP PubMed journals CDP medical journals CDP free journals CDP best journals CDP top journals CDP free medical journals CDP famous journals CDP Google Scholar indexed journals microscopic disorder articles microscopic disorder Research articles microscopic disorder review articles microscopic disorder PubMed articles microscopic disorder PubMed Central articles microscopic disorder 2023 articles microscopic disorder 2024 articles microscopic disorder Scopus articles microscopic disorder impact factor journals microscopic disorder Scopus journals microscopic disorder PubMed journals microscopic disorder medical journals microscopic disorder free journals microscopic disorder best journals microscopic disorder top journals microscopic disorder free medical journals microscopic disorder famous journals microscopic disorder Google Scholar indexed journals complex biological systems articles complex biological systems Research articles complex biological systems review articles complex biological systems PubMed articles complex biological systems PubMed Central articles complex biological systems 2023 articles complex biological systems 2024 articles complex biological systems Scopus articles complex biological systems impact factor journals complex biological systems Scopus journals complex biological systems PubMed journals complex biological systems medical journals complex biological systems free journals complex biological systems best journals complex biological systems top journals complex biological systems free medical journals complex biological systems famous journals complex biological systems Google Scholar indexed journals mRNA articles mRNA Research articles mRNA review articles mRNA PubMed articles mRNA PubMed Central articles mRNA 2023 articles mRNA 2024 articles mRNA Scopus articles mRNA impact factor journals mRNA Scopus journals mRNA PubMed journals mRNA medical journals mRNA free journals mRNA best journals mRNA top journals mRNA free medical journals mRNA famous journals mRNA Google Scholar indexed journals morphogenesis articles morphogenesis Research articles morphogenesis review articles morphogenesis PubMed articles morphogenesis PubMed Central articles morphogenesis 2023 articles morphogenesis 2024 articles morphogenesis Scopus articles morphogenesis impact factor journals morphogenesis Scopus journals morphogenesis PubMed journals morphogenesis medical journals morphogenesis free journals morphogenesis best journals morphogenesis top journals morphogenesis free medical journals morphogenesis famous journals morphogenesis Google Scholar indexed journals

Article Details

1. Introduction

Improving the efficacy of complex biological systems and overcoming malfunctions are significant challenges. The constrained disorder principle (CDP) defines the function of living systems based on their degree of variability [1]. The CDP serves as a basis for newly developed algorithms for overcoming malfunctions.

Alan Turing formulated a model that explained how random fluctuations drive the emergence of patterns and structures from initial uniformity [2]. The notion evolved is that the appearance of pattern and form in a system is far from its equilibrium state characterizes many natural processes. Turing's model explains this course, identifying a general mechanism for generating order from macroscopic uniformity and microscopic disorder [3].

The present paper describes a CDP-based artificial intelligence (AI)-system for improving effectiveness and correcting malfunctions of complex biological systems based on implementing variability signatures.

2. Methods

We reviewed the relevant studies on variability in complex biological systems and the concept of using CDP-based platforms in biology. We describe the data on the use of CDP-based second-generation AI systems for improving the function of systems, describing several clinical trials where using these platforms improved biological functionality.

3. Results

3.1 Variability is inherent to complex biological systems

Variability and randomness are inherent properties of complex biological systems4. Variability is the constantly oscillating output of a system having random or quasi-random behavior, and randomness refers to a system's irregular, complex, and unpredictable behavior [4,5]. These properties characterize all levels of the biological organization hierarchy, from the molecular, subcellular, and cellular levels to complex organs and systems, as well as in biological development and evolution processes. [1 4, 6-10]

At the genome level, the three-dimensional organization of genomes in the cell nucleus arises from DNA loops, chromatin domains, and higher-order compartments characterized by stochasticity in transcription and variability of chromatin architecture in individual cells [11]. The stochasticity in genome organization works parallel to the deterministic processes and has functional implications. Gene expression in individual cells is stochastic, reflected by cell-to-cell variability or noise in proteins [12]. It is reflected by the number of mRNA copies of a particular gene which varies among cells of the same tissue and across time within a single cell under constant environmental conditions [4,13]. Variability in gene expression is one of the reasons for the phenotypic diversity of genetically identical cells and incomplete penetrance of genetic diseases at the organism level [4,11,14]. The highly variable genes are responsible for the specific cell-type specialized functions and do not overlap between different cell types [13]. It implies that variability is a non-incidental property of a biological system crucial for its fundamental functions.

Randomness in morphogenesis instability, biological rhythms (e.g., cell-cycle period), and asymmetric cell divisions generate intercellular variability in proteins [4,5,10,13-17]. Numerous mRNA/proteins are expressed at low-copy numbers, and significant errors are incurred in partitioning molecules between two daughter cells [18].

There are several formulas for assessing the noise in protein levels when the cell-cycle duration follows a general class of probability distributions [19, 20]. The total noise is decomposed into components from stochastic expression, partitioning errors at the time of cell division, and random cell-division events. Random cell-division times generate additional extrinsic noise and critically affect the mean protein copy numbers and intrinsic noise components [21].

At the cellular level, microtubules (MTs) have unique dynamic properties, and at any point in time, a subset of MTs is rapidly growing while others are shrinking [22]. Individual microtubules switch randomly between growing and shrinking states, sometimes changing repeatedly in their lifetime. The combination of growth, shrinkage, and rapid transitions is called dynamic instability [23]. Dynamic instability has functional implications, allowing the cell to reorganize the cytoskeleton rapidly when necessary. MTs growth and shrinkage are active processes, consuming energy so that MTs adapt quickly to environmental changes and cellular needs [24-28].

Numerous biochemical processes involve variability, essential for their function under continuously changing perturbations [29]. An inherent disorder characterizes immune responses and the effect of immunomodulatory interventions, a mechanism for the flexibility necessary for generating a proper response to antigens [29-33].

Heart rate variability (HRV) is an example of the importance of variability for proper organ functions, evolving from the balance between the autonomic sympathetic and para-sympathetic nervous systems [34,35]. Variability characterizes bone's geometrical microarchitecture during the organism's development [10], blood pressure [36], breathing [37], and gait [38]. Variability in complex systems, such as the central nervous system, arises from the independent contribution of the individual cells' variabilities and the variability in intercellular connections originating from spatiotemporal configurations, grouped firing, and heterogeneous cross-correlations [4,39-42]. From an evolutionary perspective, randomness and variability underlie the diversity of life and provide the platform for selection to occur [43].

Defective engineering in non-living systems is an example of the advantages of having a degree of variability that can improve product functions [44,45]. The inherent variability of all biological systems levels is an example of the need for noise for proper functions [4,6-9 24-29,32 ,33 ,46].

3.2 Randomness in biology does eliminate order

Randomness in biological systems does not eliminate the existence of order, regularity, and organization. Process unpredictable outcomes are constrained and generated from a restricted repertoire of possibilities or under limitations, which show variability over time [10,16] The stochastic nature of biology does not imply that the outcome of a process is entirely random. An underlying disorder may be essential for higher degree order via a selection process. A healthy immune system function is based on an uncountable assortment of randomly generated antibodies originating from a limited set of genes. The selected epitope-directed immune element operates at a specific time according to the environmental circumstances [43,47].

3.3 The constrained disorder principle defines complex systems by their degree of inherent disorder

The constrained disorder principle (CDP) defines biological systems based on their degree of variability constrained within dynamic borders [48]. The degree of disorder differentiates living from non-living systems. Non-living systems are characterized by a relatively low degree of disorder and narrow borders, while living organisms have a high degree of disorder within broad dynamic boundaries, enabling adaptability to the continuous changes in the internal and external environments [48].

Per the CDP, the boundaries of a system express its function and efficiency. Systems malfunctions are defined as narrow boundaries limiting the degree of disorder to an insufficient level for dealing with dynamic perturbations in the environment or in cases where the degree of disorder is too high getting out of the boundaries, reducing system functionality [1].

Healthy biological systems operate at the "edge of chaos," not necessarily aiming to maintain steadiness under every condition, as indicated by their oscillating output even under resting conditions [4,5,8]. These fluctuations represent the interactions of multiple regulatory mechanisms. This dynamicity enables flexibility, which is essential for the proper function of a nonlinear transformable system in an unstable environment.

3.4 The steps of generating CDP-based second-generation artificial intelligence systems

First-generation artificial intelligence (AI) systems are designed to look into big data sets and analyze them for developing diagnostic and prognostic schemes. These algorithms are affected by biases related to the data, end-users, and the dynamic nature of biological systems 49. Numerous first-generation apps remind patients to take their medications to improve adherence. However, the low engagement of patients and physicians in using these systems remains a significant challenge [50-52].

Second-generation AI systems are generated based on the CDP for controlling complex systems' degree of variability to overcome malfunctions and improve efficacy [5]. These systems personalize the output based on continuously dynamic inputs from the subject and the environment. These systems are focused on meaningful endpoints, ensuring improved engagement by end users [5,53,54].

Second-generation AI systems are created in three steps [5,55,56]. In the first step, implementing variability into a system is conducted. It is an open-loop system, and no feedback is received. Implementing randomness improves function in cases where the degree of disorder is too low due to narrow non-flexible borders. In the second stage, a closed-loop system is implemented where the degree of the disorder is adapted based on pre-defined endpoints. Feedback is received from the end-user and the environment. In the third step, the algorithm receives quantifications of variability signatures from a targeted system and implements them into machine learning to improve the pre-defined endpoint. These signatures are personalized cell or organ variabilities, where variability is quantified and implemented into the algorithm. Second-generation AI systems' dynamicity enables them to adapt continuously to internal and external changes.

Figure 1 presents the three steps for using variability to overcome malfunctions in complex biological systems by providing a method for regulating the degree of variability in a targeted system in a personalized way.


Figure 1: A schematic presentation of the three steps for using variability to overcome malfunctions in complex biological systems using a method for regulating the degree of variability in a personalized way.

3.5 CDP-based second-generation artificial intelligence systems for overcoming drug resistance in subjects with chronic conditions

Prescribing medication stationary and regularly does not capture inter and intra-individual variability in drug response and disease course; thus, it may not be suitable for all patients [55,56]. In contrast, constant alterations in timing and dosing of drug administration within a physician-approved range may overcome and correspond to the system's integral variability, thus improving the drug effect [4]. In patients with chronic diseases, including heart failure, hypertension, epilepsy, depression, cancer, chronic pain, and inflammatory diseases, loss of response to chronic medications is a significant challenge [57].

The CDP provides a platform for refining and optimizing interventions. Drug holidays and arbitrary alterations (dose escalation or reduction) in drug administration are variability-based methods for improving medical therapies. Alternate-day statin prescription showed no difference in LDL-C reduction and equal tolerability compared to daily-dosing statins [58]. Implementing variability into anti-cancer therapies by alternating periods of drug cessation and re-administration delays the emergence of drug resistance [59-62]. Variable dosing intervals of basal insulin in diabetic patients showed similar efficacy compared to fixed-time insulin administration [63,64].

The second-generation AI system serves as a basis for the digital pill, which comprises any drug the algorithm regulates its administration [55]. The digital platform is used for improving response to chronic medications. The digital pill is designed to regain the drug's effect and improve the response to drugs by implementing variability into therapeutic regimens in a personalized way [29,54,65-83].

The second-generation AI-based digital pill involves three steps based on the above-described scheme. In the first stage, physicians provide subjects with an app with a range for drug administration within the therapeutic window of that medication. The app reminds the patient to take the drug at different times and dosages within the pre-defined range. The algorithm comprises a pseudorandom number generator that randomly selects a dose and time of administration from a physician's range.

The following three clinical trials exemplified the benefits of using the first step of a second-generation AI system in patients with chronic diseases.

In patients with congestive heart failure, in whom diuretics are the primary therapy, the development of resistance is widespread [84]. Using the digital pill in patients with heart failure and severe diuretic resistance improved the clinical and laboratory parameters, reducing admissions and hospitalizations due to heart failure exacerbations [85]. A high engagement rate of patients and providers to the app was documented, as users experienced a significant clinical improvement when taking their diuretics as their cell phone app instructed.

In patients with multiple sclerosis treated with Tacfidera, drug tolerance, a lack of maximal response, and prohibiting side effects limit its use [86,87]. The digital pill in these patients stabilizes disease progression and improves clinical parameters. A high engagement rate of patients with the app was recorded during the study. (unpublished). The administration of medical cannabis suffers from a lack of adherence, inability to titrate the dosage, and loss of effectiveness [80]. In a real-world follow-up of patients who received prescribed medical cannabis for chronic pain due to multiple indications, digital medical cannabis increased patients' adherence to the treatment regimens and improved the clinical response assessed by the pain score (unpublished).

A closed loop is implemented in the second level of the algorithm, which collects data on the pre-defined clinically meaningful endpoint. The algorithm adapts the variability in dosages and administration times, a chronobiology-dependent effect, to the endpoint in a personalized way. As an example, it can identify that males should receive a drug within 5-9 PM to achieve a better clinical response. As the algorithm is personalized and dynamic, it changes the regimen provided to patients over time.

For the third level, personalized biological variability signatures are quantified and implemented into the algorithm. These include HRV in patients with cardiac disorders, variability in cytokine secretion in patients with arthritis or inflammatory bowel disease, blood pressure variability in patients with hypertension, and breathing variability in patients with chronic lung disorders5 [55,56]. 

Using variability signatures to generate a relevant time-specific personalized patient-disease-drug-environment-based output presents a clinical challenge. The algorithm aims to bring the system to a desired physiological endpoint by up or down-regulating the external variability source based on alterations in internal variability signatures. These signatures are inferred from the patient's and physician's reports. By applying them to the deep learning algorithm, the system determines the optimal strengths and intervals of drug administration. The second-generation AI system can operate using only single patient continuous data [56]. However, the system can refine its performance and precision based on other users' data and the patient's response [56].

Figure 2 illustrates some of the variability-inspired disease-specific indices that can be used for the third stage of the algorithm.


Figure 2: A schematic presentation of several variability-inspired disease-specific indices that can be used for the third stage of the algorithm

3.6 A scheme for improving the effectiveness of biological systems by implementing quantified variability signatures

The malfunction of systems remains a significant challenge in all areas. Differentiating the inherent variability required and the noise resulting from malfunctions is mandatory. This differentiation is one of the most significant challenges when looking into malfunctions of biological systems. Per the CDP, "unwanted noise" represents a high degree of variability outside the borders that must be regulated.

Figure 3 presents a scheme for overcoming malfunctions and improving the effectiveness of biological systems by differentiating between inherent variability and unwanted noise. The system implements machinery to regulate the disorder, increasing it when it is low and reducing disturbing unwanted noise.


Figure 3: A pictorial presentation for overcoming malfunctions and improving the effectiveness of biological systems by differentiating between inherent variability and unwanted noise. The system implements machinery to regulate the disorder, increasing it when it is low and reducing disturbing unwanted noise.

In summary, the CDP provides a platform for defining biological systems and improving disturbed systems' functions. The use of CDP-based second-generation AI systems is a promising method for making use of noise in a biological system. Ongoing studies are expected to improve the ability to differentiate the inherent variability from unwanted noise in complex systems, which can further improve the algorithm.






CDP: constrained disorder principle; AI: artificial intelligence; MT: microtubules; EDSS: The Kurtzke Expanded Disability Status Scale


YI is the founder of Oberon Sciences.


  1. Ilan Y. The constrained disorder principle defines living organisms and provides a method for correcting disturbed biological systems. Comput Struct Biotechnol J 20 (2022): 6087-96.
  2. AM TURING F. The chemical basis of morphogenesis. Sciences-cecm usp br 237 (1952): 37-72.
  3. Ball P. Forging patterns and making waves from biology to geology: a commentary on Turing (1952) 'The chemical basis of morphogenesis'. Philosophical transactions of the Royal Society of London Series B, Biological sciences 370(1666) (2015).
  4. Ilan Y. Order Through Disorder: The Characteristic Variability of Systems. Front Cell Dev Biol 8(2020): 186.
  5. Ilan Y. Second-Generation Digital Health Platforms: Placing the Patient at the Center and Focusing on Clinical Outcomes. Front Digit Health 2(2020): 569178.
  6. Ilan Y. Overcoming randomness does not rule out the importance of inherent randomness for functionality. J Biosci 44(6) (2019).
  7. Ilan Y. Generating randomness: making the most out of disordering a false order into a real one. J Transl Med 17(1) (2019): 49.
  8. Ilan Y. Advanced Tailored Randomness: A Novel Approach for Improving the Efficacy of Biological Systems. J Comput Biol 27(1) (2020): 20-29.
  9. Pottier P, Burke S, Zhang RY, et al. Developmental plasticity in thermal tolerance: Ontogenetic variation, persistence, and future directions. Ecol Lett 25(10) (2022): 2245-68.
  10. Montevil M, Mossio M, Pocheville A, et al. Theoretical principles for biology: Variation. Prog Biophys Mol Biol 122(1) (2016): 36-50.
  11. Finn EH, Misteli T. Molecular basis and biological function of variability in spatial genome organization. Science 365(6457) (2019).
  12. Raser JM, O'Shea EK. Noise in gene expression: origins, consequences, and control. Science 309(5743) (2005): 2010-3.
  13. Osorio D, Yu X, Zhong Y, et al. Single-Cell Expression Variability Implies Cell Function. Cells 9(1) (2019).
  14. Pelkmans L. Cell Biology. Using cell-to-cell variability--a new era in molecular biology. Science 336(6080) (2012): 425-6.
  15. Roberfroid S, Vanderleyden J, Steenackers H. Gene expression variability in clonal populations: Causes and consequences. Crit Rev Microbiol 42(6) (2016): 969-84.
  16. Theise ND, Harris R. Postmodern biology: (adult) (stem) cells are plastic, stochastic, complex, and uncertain. Handb Exp Pharmacol 2006(174): 389-408.
  17. Colak R, Kim T, Michaut M, et al. Distinct types of disorder in the human proteome: functional implications for alternative splicing. PLoS Comput Biol 9(4) (2013): e1003030.
  18. Soltani M, Singh A. Effects of cell-cycle-dependent expression on random fluctuations in protein levels. Royal Society open science 3(12) (2016): 160578.
  19. Cardoso AR, Lopes-Marques M, Oliveira M, et al. Genetic Variability of the Functional Domains of Chromodomains Helicase DNA-Binding (CHD) Proteins. Genes (Basel) 12(11) (2021).
  20. Soltani M, Vargas-Garcia CA, Antunes D, et al. Intercellular Variability in Protein Levels from Stochastic Expression and Noisy Cell Cycle Processes. PLoS Comput Biol 12(8) (2016): e1004972.
  21. Soltani M, Vargas-Garcia CA, Antunes D, et al. Intercellular Variability in Protein Levels from Stochastic Expression and Noisy Cell Cycle Processes. PLOS Computational Biology 12(8) (2016): e1004972.
  22. Burbank KS, Mitchison TJ. Microtubule dynamic instability. Curr Biol 16(14) (2006): R516-7.
  23. Mitchison T, Kirschner M. Dynamic instability of microtubule growth. Nature 312(5991) (1984): 237-42.
  24. Ilan Y. Randomness in microtubule dynamics: an error that requires correction or an inherent plasticity required for normal cellular function? Cell Biol Int 43(7) (2019): 739-48.
  25. Ilan Y. Microtubules: From understanding their dynamics to using them as potential therapeutic targets. J Cell Physiol 234(6) (2019): 7923-37.
  26. Ilan-Ber T, Ilan Y. The role of microtubules in the immune system and as potential targets for gut-based immunotherapy. Mol Immunol 111(2019): 73-82.
  27. Forkosh E, Kenig A, Ilan Y. Introducing variability in targeting the microtubules: Review of current mechanisms and future directions in colchicine therapy. Pharmacol Res Perspect 8(4) (2020): e00616.
  28. Ilan Y. Microtubules as a potential platform for energy transfer in biological systems: a target for implementing individualized, dynamic variability patterns to improve organ function. Molecular and cellular biochemistry 478(2) (2022): 375-392.
  29. Ilan Y. beta-Glycosphingolipids as Mediators of Both Inflammation and Immune Tolerance: A Manifestation of Randomness in Biological Systems. Front Immunol 10 (2019): 1143.
  30. Noble R, Noble D. Harnessing stochasticity: How do organisms make choices? Chaos 28(10) (2018): 106309.
  31. Shabat Y, Lichtenstein Y, Ilan Y. Short-Term Cohousing of Sick with Healthy or Treated Mice Alleviates the Inflammatory Response and Liver Damage. Inflammation 44(2) (2021): 518-25.
  32. El-Haj M, Kanovitch D, Ilan Y. Personalized inherent randomness of the immune system is manifested by an individualized response to immune triggers and immunomodulatory therapies: a novel platform for designing personalized immunotherapies. Immunol Res 67(4-5) (2019): 337-47.
  33. Noble D. The role of stochasticity in biological communication processes. Progress in biophysics and molecular biology 162 (2021): 122-28.
  34. Chiera M, Cerritelli F, Casini A, et al. Heart Rate Variability in the Perinatal Period: A Critical and Conceptual Review. Front Neurosci 14(2020): 561186
  35. Forte G, Favieri F, Casagrande M. Heart Rate Variability and Cognitive Function: A Systematic Review. Front Neurosci 13 (2019): 710.
  36. Schutte AE, Kollias A, Stergiou GS. Blood pressure and its variability: classic and novel measurement techniques. Nat Rev Cardiol 19(10) (2022): 643-54.
  37. van den Bosch OFC, Alvarez-Jimenez R, de Grooth HJ, et al. Breathing variability-implications for anaesthesiology and intensive care. Crit Care 25(1) (2021): 280.
  38. Moon Y, Sung J, An R, et al. Gait variability in people with neurological disorders: A systematic review and meta-analysis. Hum Mov Sci 47(2016): 197-208.
  39. Genon S, Eickhoff SB, Kharabian S. Linking interindividual variability in brain structure to behaviour. Nat Rev Neurosci 23(5) (2022): 307-18.
  40. Saha S, Baumert M. Intra- and Inter-subject Variability in EEG-Based Sensorimotor Brain Computer Interface: A Review. Front Comput Neurosci 13(2019): 87.
  41. Casarotto S, Comanducci A, Rosanova M, et al. Stratification of unresponsive patients by an independently validated index of brain complexity. Ann Neurol 80(5) (2016): 718-29.
  42. Mateos DM, Guevara Erra R, Wennberg R, et al. Measures of entropy and complexity in altered states of consciousness. Cogn Neurodyn 12(1) (2018): 73-84.
  43. Ellis GFR, Kopel J. The Dynamical Emergence of Biology From Physics: Branching Causation via Biomolecules. Front Physiol 9 (2018): 1966.
  44. Attariani H, Momeni K, Adkins K. Defect Engineering: A Path toward Exceeding Perfection. ACS Omega 2(2) (2017): 663-69.
  45. Zhang N, Gao C, Xiong Y. Defect engineering: A versatile tool for tuning the activation of key molecules in photocatalytic reactions. Journal of Energy Chemistry 37(2019): 43-57.
  46. Noble D. Modern physiology vindicates Darwin's dream. Exp Physiol 107(9) (2022): 1015-28.
  47. Williams DL, Sikora VM, Hammer MA, et al. May the Odds Be Ever in Your Favor: Non-deterministic Mechanisms Diversifying Cell Surface Molecule Expression. Front Cell Dev Biol 9(2021): 720798.
  48. Ilan Y. The constrained disorder principle defines living organisms and provides a method for correcting disturbed biological systems. Computational and Structural Biotechnology Journal 20 (2022): 6087-96.
  49. Ahmad Z, Rahim S, Zubair M, et al. Artificial intelligence (AI) in medicine, current applications and future role with special emphasis on its potential and promise in pathology: present and future impact, obstacles including costs and acceptance among pathologists, practical and philosophical considerations. A comprehensive review. Diagnostic pathology 16(1) (2021): 24.
  50. Birnbaum F, Lewis D, Rosen RK, et al. Patient engagement and the design of digital health. Academic emergency medicine : official journal of the Society for Academic Emergency Medicine 22(6) (2015): 754-6.
  51. Challenges in digital medicine applications in under-resourced settings. Nature Communications 13(1) (2022): 3020.
  52. Cummins N, Schuller BW. Five Crucial Challenges in Digital Health. Frontiers in Digital Health 2 (2020): 536203.
  53. Hurvitz N, Azmanov H, Kesler A, et al. Establishing a second-generation artificial intelligence-based system for improving diagnosis, treatment, and monitoring of patients with rare diseases. European Journal of Human Genetics 29(10) (2021): 1485-90.
  54. Azmanov H, Bayatra A, Ilan Y. Digital Analgesic Comprising a Second-Generation Digital Health System: Increasing Effectiveness by Optimizing the Dosing and Minimizing Side Effects. J Pain Res 15(2022): 1051-60.
  55. Ilan Y. Improving Global Healthcare and Reducing Costs Using Second-Generation Artificial Intelligence-Based Digital Pills: A Market Disruptor. Int J Environ Res Public Health 18(2) (2021): 811.
  56. Ilan Y. Next-Generation Personalized Medicine: Implementation of Variability Patterns for Overcoming Drug Resistance in Chronic Diseases. J Pers Med 12(8) (2022): 1303.
  57. Ilan Y. Overcoming Compensatory Mechanisms toward Chronic Drug Administration to Ensure Long-Term, Sustainable Beneficial Effects. Mol Ther Methods Clin Dev 18 (2020): 335-44.
  58. Awad K, Mikhailidis DP, Toth PP, et al. Efficacy and Safety of Alternate-Day Versus Daily Dosing of Statins: a Systematic Review and Meta-Analysis. Cardiovasc Drugs Ther 31(4) (2017): 419-31.
  59. Kavran AJ, Stuart SA, Hayashi KR, et al. Intermittent treatment of BRAF(V600E) melanoma cells delays resistance by adaptive resensitization to drug rechallenge. Proc Natl Acad Sci USA 119(12) (2022): e2113535119.
  60. Gonzalez-Cao M, Mayo de Las Casas C, Oramas J, et al. Intermittent BRAF inhibition in advanced BRAF mutated melanoma results of a phase II randomized trial. Nat Commun 12(1) (2021): 7008.
  61. Perera M, Roberts MJ, Klotz L, et al. Intermittent versus continuous androgen deprivation therapy for advanced prostate cancer. Nat Rev Urol 17(8) (2020): 469-81.
  62. Tonini G, Imperatori M, Vincenzi B, et al. Rechallenge therapy and treatment holiday: different strategies in management of metastatic colorectal cancer. J Exp Clin Cancer Res 32(1) (2013): 92.
  63. Garg S, Selam JL, Bhargava A, et al. Similar HbA1c reduction and hypoglycaemia with variable- vs fixed-time dosing of basal insulin peglispro in type 1 diabetes: IMAGINE 7 study. Diabetes Obes Metab 18(2) (2016): 43-49.
  64. Riddle MC, Bolli GB, Home PD, et al. Efficacy and Safety of Flexible Versus Fixed Dosing Intervals of Insulin Glargine 300 U/mL in People with Type 2 Diabetes. Diabetes Technol Ther 18(4) (2016): 252-7.
  65. Kessler A, Weksler-Zangen S, Ilan Y. Role of the Immune System and the Circadian Rhythm in the Pathogenesis of Chronic Pancreatitis: Establishing a Personalized Signature for Improving the Effect of Immunotherapies for Chronic Pancreatitis. Pancreas 49(8) (2020): 1024-32.
  66. Ishay Y, Kolben Y, Kessler A, et al. Role of circadian rhythm and autonomic nervous system in liver function: a hypothetical basis for improving the management of hepatic encephalopathy. Am J Physiol Gastrointest Liver Physiol 321(4) (2021): G400-G12.
  67. Kolben Y, Weksler-Zangen S, Ilan Y. Adropin as a potential mediator of the metabolic system-autonomic nervous system-chronobiology axis: Implementing a personalized signature-based platform for chronotherapy. Obes Rev 22(2) (2021): e13108.
  68. Kenig A, Kolben Y, Asleh R, et al. Improving Diuretic Response in Heart Failure by Implementing a Patient-Tailored Variability and Chronotherapy-Guided Algorithm. Front Cardiovasc Med 8 (2021): 695547.
  69. Azmanov H, Ross EL, Ilan Y. Establishment of an Individualized Chronotherapy, Autonomic Nervous System, and Variability-Based Dynamic Platform for Overcoming the Loss of Response to Analgesics. Pain Physician 24(3) (2021): 243-52.
  70. Potruch A, Khoury ST, Ilan Y. The role of chronobiology in drug-resistance epilepsy: The potential use of a variability and chronotherapy-based individualized platform for improving the response to anti-seizure drugs. Seizure 80(2020): 201-11.
  71. Isahy Y, Ilan Y. Improving the long-term response to antidepressants by establishing an individualized platform based on variability and chronotherapy. Int J Clin Pharmacol Ther 59(12) (2021): 768-74.
  72. Khoury T, Ilan Y. Introducing Patterns of Variability for Overcoming Compensatory Adaptation of the Immune System to Immunomodulatory Agents: A Novel Method for Improving Clinical Response to Anti-TNF Therapies. Front Immunol 10(2019): 2726.
  73. Khoury T, Ilan Y. Platform introducing individually tailored variability in nerve stimulations and dietary regimen to prevent weight regain following weight loss in patients with obesity. Obes Res Clin Pract 15(2) (2021): 114-23.
  74. Kenig A, Ilan Y. A Personalized Signature and Chronotherapy-Based Platform for Improving the Efficacy of Sepsis Treatment. Front Physiol 10 (2019): 1542.
  75. Ilan Y. Why targeting the microbiome is not so successful: can randomness overcome the adaptation that occurs following gut manipulation? Clin Exp Gastroenterol 12(2019): 209-17.
  76. Gelman R, Bayatra A, Kessler A, et al. Targeting SARS-CoV-2 receptors as a means for reducing infectivity and improving antiviral and immune response: an algorithm-based method for overcoming resistance to antiviral agents. Emerg Microbes Infect 9(1) (2020): 1397-406.
  77. Ishay Y, Potruch A, Schwartz A, et al. A digital health platform for assisting the diagnosis and monitoring of COVID-19 progression: An adjuvant approach for augmenting the antiviral response and mitigating the immune-mediated target organ damage. Biomed Pharmacother 143 (2021): 112228.
  78. Ilan Y, Spigelman Z. Establishing patient-tailored variability-based paradigms for anti-cancer therapy: Using the inherent trajectories which underlie cancer for overcoming drug resistance. Cancer Treat Res Commun 25(2020): 100240.
  79. Hurvitz N, Azmanov H, Kesler A, et al. Establishing a second-generation artificial intelligence-based system for improving diagnosis, treatment, and monitoring of patients with rare diseases. Eur J Hum Genet 29(10) (2021): 1485-90.
  80. Ilan Y. Digital Medical Cannabis as Market Differentiator: Second-Generation Artificial Intelligence Systems to Improve Response. Front Med (Lausanne) 8 (2021): 788777.
  81. Gelman R, Berg M, Ilan Y. A Subject-Tailored Variability-Based Platform for Overcoming the Plateau Effect in Sports Training: A Narrative Review. Int J Environ Res Public Health 19(3) (2022): 1722.
  82. Hurvitz N, Elkhateeb N, Sigawi T, et al. Improving the effectiveness of anti-aging modalities by using the constrained disorder principle-based management algorithms. Frontiers in Aging 3 (2022): 1044038.
  83. Kolben Y, Azmanov H, Gelman R, et al. Using chronobiology-based second-generation artificial intelligence digital system for overcoming antimicrobial drug resistance in chronic infections. Ann Med 55(1) (2023): 311-18.
  84. Shams E, Bonnice S, Mayrovitz HN. Diuretic Resistance Associated With Heart Failure. Cureus 14(1) (2022): e21369.
  85. Gelman R, Hurvitz N, Nesserat R, et al. A second-generation artificial intelligence-based therapeutic regimen improves diuretic resistance in heart failure: Results of a feasibility open-labeled clinical trial. Biomedicine & Pharmacotherapy 161(2023): 114334.
  86. Kresa-Reahl K, Repovic P, Robertson D, et al. Effectiveness of Delayed-release Dimethyl Fumarate on Clinical and Patient-reported Outcomes in Patients With Relapsing Multiple Sclerosis Switching From Glatiramer Acetate: RESPOND, a Prospective Observational Study. Clin Ther 40(12) (2018): 2077-87.
  87. Narapureddy B, Dubey D. Clinical evaluation of dimethyl fumarate for the treatment of relapsing-remitting multiple sclerosis: efficacy, safety, patient experience and adherence. Patient Prefer Adherence 13(2019): 1655-66.

© 2016-2024, Copyrights Fortune Journals. All Rights Reserved